First convolution network on MNIST database.
Note: install tqdm if not installed: !pip install tqdm
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
print("torch", torch.__version__)
from torchvision import datasets, transforms
from tqdm import tqdm
torch 1.5.0+cpu
%matplotlib inline
BATCH_SIZE = 64
TEST_BATCH_SIZE = 64
DATA_DIR = 'data/'
USE_CUDA = True
N_EPOCHS = 50
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(DATA_DIR, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(DATA_DIR, train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=TEST_BATCH_SIZE, shuffle=True)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=-1)
model = Net()
if USE_CUDA:
try:
model = model.cuda()
except Exception as e:
print(e)
USE_CUDA = False
N_EPOCHS = 3
Torch not compiled with CUDA enabled
optimizer = optim.Adam(model.parameters())
def train(epoch, verbose=True):
model.train()
losses = []
loader = tqdm(train_loader, total=len(train_loader))
for batch_idx, (data, target) in enumerate(loader):
if USE_CUDA:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
losses.append(float(loss.item()))
if verbose and batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
return np.mean(losses)
def test(verbose=True):
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
if USE_CUDA:
data, target = data.cuda(), target.cuda()
with torch.no_grad():
data = Variable(data)
target = Variable(target)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()
test_loss /= len(test_loader.dataset)
if verbose:
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
return [float(test_loss), correct]
perfs = []
for epoch in range(1, N_EPOCHS + 1):
t0 = time.time()
train_loss = train(epoch, verbose=False)
test_loss, correct = test(verbose=False)
perfs.append([epoch, train_loss, test_loss, correct, len(test_loader.dataset), time.time() - t0])
print("epoch {}: train loss {:.4f}, test loss {:.4f}, accuracy {}/{} in {:.2f}s".format(*perfs[-1]))
100%|██████████| 938/938 [00:43<00:00, 23.07it/s]
epoch 1: train loss 0.5491, test loss 0.1113, accuracy 9673/10000 in 46.74s
100%|██████████| 938/938 [00:39<00:00, 25.41it/s]
epoch 2: train loss 0.2752, test loss 0.0783, accuracy 9759/10000 in 42.01s
100%|██████████| 938/938 [00:38<00:00, 24.14it/s]
epoch 3: train loss 0.2232, test loss 0.0682, accuracy 9800/10000 in 41.69s
df_perfs = pd.DataFrame(perfs, columns=["epoch", "train_loss", "test_loss", "accuracy", "n_test", "time"])
df_perfs
epoch | train_loss | test_loss | accuracy | n_test | time | |
---|---|---|---|---|---|---|
0 | 1 | 0.549102 | 0.111298 | 9673 | 10000 | 46.742926 |
1 | 2 | 0.275170 | 0.078345 | 9759 | 10000 | 42.009596 |
2 | 3 | 0.223219 | 0.068236 | 9800 | 10000 | 41.691449 |
df_perfs[["train_loss", "test_loss"]].plot();
df_perfs[["train_loss", "test_loss"]].plot(ylim=(0, 0.2));